instances/50_Math_Problem_Solving_Transformer_DeepMindMath_DL.json

{
    "name": "50_Math_Problem_Solving_Transformer_DeepMindMath_DL",
    "query": "Hi! I need help with a project that uses a Transformer model to solve math problems from the DeepMind Mathematics dataset. Please load the dataset and preprocessing it in `src/data_loader.py`. The preprocessing should parse and standardize the math expressions in a syntactically consistent way so the model can easily process them. Implement the Transformer in `src/model.py`. Also, tune the hyperparameters such as the learning rate and the batch size in `src/train.py`, and save the training loss curve to `results/figures/training_loss_curve.png`. Sample and save some Transformer generated solutions in `results/sample_solutions.txt`. Using your model, create a simple interactive tool with Gradio or Streamlit in `src/interface.py` that can solve various user given math problems. Lastly, generate a report on how the model performs with different types of problems, including model accuracy, error analysis, and future improvement suggestions. Save it as `results/metrics/model_report.md`. Thanks in advance!",
    "tags": [
        "Natural Language Processing"
    ],
    "requirements": [
        {
            "requirement_id": 0,
            "prerequisites": [],
            "criteria": "DeepMind Mathematics dataset is loaded in `src/data_loader.py`.",
            "category": "Dataset or Environment",
            "satisfied": null
        },
        {
            "requirement_id": 1,
            "prerequisites": [
                0
            ],
            "criteria": "Data preprocessing is performed including parsing and standardizing mathematical expressions in `src/data_loader.py`.",
            "category": "Data preprocessing and postprocessing",
            "satisfied": null
        },
        {
            "requirement_id": 2,
            "prerequisites": [],
            "criteria": "A \"Transformer\" model is implemented in `src/model.py`.",
            "category": "Machine Learning Method",
            "satisfied": null
        },
        {
            "requirement_id": 3,
            "prerequisites": [
                0,
                1,
                2
            ],
            "criteria": "Hyperparameters such as learning rate and batch size are tuned in `src/train.py`.",
            "category": "Machine Learning Method",
            "satisfied": null
        },
        {
            "requirement_id": 4,
            "prerequisites": [
                0,
                1,
                2,
                3
            ],
            "criteria": "Model training loss curve is saved as `results/figures/training_loss_curve.png`.",
            "category": "Visualization",
            "satisfied": null
        },
        {
            "requirement_id": 5,
            "prerequisites": [
                0,
                1,
                2,
                3
            ],
            "criteria": "Some Transformer generated solutions are saved in `results/sample_solutions.txt`.",
            "category": "Other",
            "satisfied": null
        },
        {
            "requirement_id": 6,
            "prerequisites": [
                0,
                1,
                2,
                3
            ],
            "criteria": "An interactive tool is created allowing users to input mathematical problems and receive solutions using \"Gradio\" or \"Streamlit\" in `src/interface.py`.",
            "category": "Human Computer Interaction",
            "satisfied": null
        },
        {
            "requirement_id": 7,
            "prerequisites": [
                0,
                1,
                2,
                3,
                4
            ],
            "criteria": "A report is generated containing model accuracy, error analysis, and future improvement suggestions, and saved as `results/metrics/model_report.md`.",
            "category": "Other",
            "satisfied": null
        }
    ],
    "preferences": [
        {
            "preference_id": 0,
            "criteria": "The preprocessing step should ensure that the mathematical expressions are standardized in a way that makes them easily processed by the model.",
            "satisfied": null
        },
        {
            "preference_id": 1,
            "criteria": "The interactive tool should be capable of handling a wide variety of mathematical problem types.",
            "satisfied": null
        },
        {
            "preference_id": 2,
            "criteria": "The report should provide insights into how the model handles different types of mathematical problems, identifying specific strengths and areas for improvement.",
            "satisfied": null
        }
    ],
    "is_kaggle_api_needed": false,
    "is_training_needed": true,
    "is_web_navigation_needed": false
}